{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# RQ5 补充统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import copy"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "[('/api/google-maps', '/api/twitter', 0, 151, 184, 0.82),\n ('/api/twitter', '/api/google-maps', 0, 151, 184, 0.82),\n ('/api/flickr', '/api/youtube', 0, 139, 184, 0.76),\n ('/api/youtube', '/api/flickr', 0, 139, 184, 0.76),\n ('/api/facebook', '/api/twitter', 0, 159, 180, 0.88)]"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "with open('graph_data_backup/all_pairs_and_usage.txt', 'r', encoding='utf-8') as file:\n",
    "    file.readline()     # 读掉第一行\n",
    "    for line in file:\n",
    "        data.append(eval(line))\n",
    "data[:5]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "[('/api/twitter', '/api/google-maps', 0, 62, 184, 0.0),\n ('/api/flickr', '/api/youtube', 0, 36, 184, 0.0),\n ('/api/twitter', '/api/facebook', 0, 64, 179, 0.0),\n ('/api/flickr', '/api/google-maps', 0, 46, 171, 0.0),\n ('/api/google-maps', '/api/youtube', 0, 48, 157, 0.0),\n ('/api/twilio-sms', '/api/twilio', 0, 42, 152, 0.0)]"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def remove_symmetry(data):\n",
    "    # 清理数据，把对称的删除\n",
    "    data2 = copy.deepcopy(data)\n",
    "    for d in data2:\n",
    "        rev_d = (d[1], d[0], d[2], d[3], d[4], d[5])\n",
    "        if rev_d in data2:\n",
    "            data2.remove(d)\n",
    "    return data2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "绘图数据准备"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "gap = 5\n",
    "act_counter = Counter()\n",
    "all_counter = Counter()\n",
    "dea_counter = Counter()\n",
    "for d in data:\n",
    "    act = d[3]\n",
    "    all = d[4]\n",
    "\n",
    "    level = all - (all % gap)\n",
    "\n",
    "    act_counter[level] += act\n",
    "    all_counter[level] += all\n",
    "    dea_counter[level] += all - act"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "存活率图像"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x2ae25e3f310>]"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1728x576 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [str(k) + '~' + str(k + gap) for k in reversed(list(act_counter.keys()))]    # all_counter 也可\n",
    "y = [act_counter[k] / all_counter[k] for k in reversed(list(act_counter.keys()))]\n",
    "\n",
    "plt.figure(figsize=(24, 8))\n",
    "plt.xlabel('Used count')\n",
    "plt.ylabel('Active Mashup Rate')\n",
    "plt.plot(x, y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "死亡率图像"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x2c4c4501700>]"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1728x576 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [str(k) + '~' + str(k + 5) for k in reversed(list(act_counter.keys()))]    # all_counter 也可\n",
    "y = [dea_counter[k] / all_counter[k] for k in reversed(list(act_counter.keys()))]\n",
    "\n",
    "plt.figure(figsize=(24, 8))\n",
    "plt.xlabel('API Pair usage')\n",
    "plt.ylabel('Used by Dead Mashups')\n",
    "plt.plot(x, y)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "读取原始数据，然后获取我们实际访问出来的存活情况"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "with open('graph_data_backup/data0906-1.txt', 'r', encoding='utf-8') as act_file:\n",
    "    act_mashups = json.load(act_file)\n",
    "    act_mashups = [m for m in act_mashups if m is not None]\n",
    "\n",
    "with open('graph_data_backup/deadpool_mashups0917.txt', 'r', encoding='utf-8') as dead_file:\n",
    "    dead_mashups = json.load(dead_file)\n",
    "    dead_mashups = [m for m in dead_mashups if m is not None]\n",
    "\n",
    "all_mashups = act_mashups + dead_mashups"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "with open('graph_data_backup/mashup_accessibility.txt', 'r') as acc:\n",
    "    accessibility = json.load(acc)\n",
    "    # accessibility = [ac for ac in accessibility if ac is not None]\n",
    "    # build a dict\n",
    "    acc_dict = {}\n",
    "    for ac in accessibility:\n",
    "        if ac is None:\n",
    "            continue\n",
    "        else:\n",
    "            if ac['is_accessible']:\n",
    "                acc_dict[ac['title']] = True\n",
    "            else:\n",
    "                acc_dict[ac['title']] = False\n",
    "\n",
    "def is_accessible(title):\n",
    "    return acc_dict.get(title, False)\n",
    "\n",
    "def produce_pair(apis, requires_reflexive=False, requires_symmetry=False):\n",
    "    pairs = set()\n",
    "    sorted_apis = sorted(apis, key=lambda x: x[0])\n",
    "    for l in sorted_apis:\n",
    "        if requires_reflexive:\n",
    "            pairs.add((l, l, 0))\n",
    "        for r in sorted_apis:\n",
    "            if l != r:\n",
    "                if requires_symmetry:\n",
    "                    pairs.add((l, r, 0))\n",
    "                else:\n",
    "                    # 不要对称的\n",
    "                    if (r, l, 0) not in pairs:\n",
    "                        pairs.add((l, r, 0))\n",
    "    return list(pairs)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 制作一些数据结构方便以后使用\n",
    "\n",
    "1. mashup_title -> [pair] (list)\n",
    "2. pair -> [mashup_title]\n",
    "3. mashup_title -> mashup_object"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "(7901, 29078)"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_pairs = {}    # mashup和他的pair\n",
    "api_pair_mashups = {}\n",
    "mashups_name_dict = {}\n",
    "for dd in all_mashups:\n",
    "    mashups_name_dict[dd['title']] = dd\n",
    "    ra_links = []\n",
    "    if dd is None:\n",
    "        continue\n",
    "    for ra in dd['related_apis']:\n",
    "        if ra is None:\n",
    "            continue\n",
    "        ra_links.append(ra['url'])\n",
    "    pairs = produce_pair(ra_links, False, True)\n",
    "    api_pairs[dd['title']] = pairs\n",
    "    for pair in pairs:\n",
    "        if pair in api_pair_mashups:\n",
    "            api_pair_mashups[pair].append(dd['title'])\n",
    "        else:\n",
    "            api_pair_mashups[pair] = [dd['title']]\n",
    "\n",
    "len(api_pairs), len(api_pair_mashups)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "api_pairs_counter_all = Counter()\n",
    "api_pairs_counter_act = Counter()\n",
    "for mashup_name in api_pairs:\n",
    "    pairs = api_pairs[mashup_name]\n",
    "    for pair in pairs:\n",
    "        api_pairs_counter_all[pair] += 1\n",
    "        if is_accessible(mashup_name):\n",
    "            api_pairs_counter_act[pair] += 1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "# 整理数据\n",
    "api_pairs_list = []\n",
    "for pair in api_pairs_counter_all.keys():\n",
    "    if pair in api_pairs_counter_act:\n",
    "        new_pair = (pair[0], pair[1], pair[2], api_pairs_counter_act[pair], api_pairs_counter_all[pair], 0.)\n",
    "    else:\n",
    "        # 不在act字典，说明没有被活着的mashup引用\n",
    "        new_pair = (pair[0], pair[1], pair[2], 0, api_pairs_counter_all[pair], 0.)\n",
    "    api_pairs_list.append(new_pair)\n",
    "api_pairs_list = sorted(api_pairs_list, key=lambda x: x[4], reverse=True)\n",
    "api_pairs_list = remove_symmetry(api_pairs_list)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "[('/api/google-maps', '/api/twitter', 0, 62, 184, 0.0),\n ('/api/twitter', '/api/google-maps', 0, 62, 184, 0.0),\n ('/api/flickr', '/api/youtube', 0, 36, 184, 0.0),\n ('/api/youtube', '/api/flickr', 0, 36, 184, 0.0),\n ('/api/facebook', '/api/twitter', 0, 64, 179, 0.0),\n ('/api/twitter', '/api/facebook', 0, 64, 179, 0.0),\n ('/api/flickr', '/api/google-maps', 0, 46, 171, 0.0),\n ('/api/google-maps', '/api/flickr', 0, 46, 171, 0.0),\n ('/api/youtube', '/api/google-maps', 0, 48, 157, 0.0),\n ('/api/google-maps', '/api/youtube', 0, 48, 157, 0.0)]"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_pairs_list[:10]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "上面的统计结果没有什么特别的结果"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "(99.856, 112.93739673214614)"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "act_count_pair = 0\n",
    "dead_count_pair = 0\n",
    "act_count_mashup = 0\n",
    "dead_count_mashup = 0\n",
    "\n",
    "for dd in all_mashups:\n",
    "    ra_links = []\n",
    "    if dd is None:\n",
    "        continue\n",
    "    for ra in dd['related_apis']:\n",
    "        if ra is None:\n",
    "            continue\n",
    "        ra_links.append(ra['url'])\n",
    "    temp_pairs = produce_pair(ra_links, False, True)\n",
    "    if is_accessible(dd['title']):\n",
    "        act_count_mashup += 1\n",
    "    else:\n",
    "        dead_count_mashup += 1\n",
    "    for pair in temp_pairs:\n",
    "        count = api_pairs_counter_all[pair]\n",
    "        if is_accessible(dd['title']):\n",
    "            act_count_pair += count\n",
    "        else:\n",
    "            dead_count_pair += count\n",
    "\n",
    "act_count_pair / float(act_count_mashup), dead_count_pair / float(dead_count_mashup)\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 10.26 统计api的被使用次数，从源文件读入即可"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "1693"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_usage_counter = Counter()\n",
    "for ma in all_mashups:\n",
    "    ras = ma['related_apis']\n",
    "    for ra in ras:\n",
    "        if ra is None:\n",
    "            continue\n",
    "        api_usage_counter[ra['url']] += 1\n",
    "\n",
    "len(api_usage_counter)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "[('/api/google-maps', 2575),\n ('/api/twitter', 827),\n ('/api/youtube', 710),\n ('/api/flickr', 635),\n ('/api/facebook', 451),\n ('/api/amazon-product-advertising', 433),\n ('/api/twilio', 361),\n ('/api/lastfm', 245),\n ('/api/ebay', 227),\n ('/api/google-search', 194)]"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(sorted(api_usage_counter.items(), key=lambda x: x[1], reverse=True))[:10]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "def get_usage(url):\n",
    "    return api_usage_counter[url]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "2575"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_usage('/api/google-maps')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "已经有了pair->mashups\n",
    "\n",
    "还需要mashup->pairs"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "[('Mashup: PropRover',\n  [('/api/twitter', '/api/google-maps', 0, 62, 184, 0.0),\n   ('/api/feedburner', '/api/twitter', 0, 2, 7, 0.0),\n   ('/api/feedburner', '/api/google-maps', 0, 2, 6, 0.0),\n   ('/api/feedburner', '/api/facebook-business-mapping', 0, 0, 1, 0.0),\n   ('/api/facebook-business-mapping', '/api/twitter', 0, 0, 1, 0.0),\n   ('/api/feedburner', '/api/google-my-business', 0, 0, 1, 0.0),\n   ('/api/google-my-business', '/api/facebook-business-mapping', 0, 0, 1, 0.0),\n   ('/api/google-maps', '/api/facebook-business-mapping', 0, 0, 1, 0.0),\n   ('/api/twitter', '/api/google-my-business', 0, 0, 1, 0.0),\n   ('/api/facebook-business-mapping', '/api/feedburner', 0, 0, 1, 0.0),\n   ('/api/google-maps', '/api/google-my-business', 0, 0, 1, 0.0)]),\n ('Mashup: We Read, We Tweet',\n  [('/api/twitter', '/api/google-maps', 0, 62, 184, 0.0),\n   ('/api/google-maps', '/api/new-york-times-article-search', 0, 0, 4, 0.0),\n   ('/api/twitter', '/api/backtweets', 0, 0, 3, 0.0),\n   ('/api/new-york-times-article-search', '/api/twitter', 0, 0, 2, 0.0),\n   ('/api/backtweets', '/api/google-maps', 0, 0, 1, 0.0),\n   ('/api/new-york-times-article-search', '/api/backtweets', 0, 0, 1, 0.0)]),\n ('Mashup: AuroraMap',\n  [('/api/twitter', '/api/google-maps', 0, 62, 184, 0.0),\n   ('/api/twitter', '/api/facebook', 0, 64, 179, 0.0),\n   ('/api/flickr', '/api/google-maps', 0, 46, 171, 0.0),\n   ('/api/facebook', '/api/google-maps', 0, 39, 111, 0.0),\n   ('/api/flickr', '/api/twitter', 0, 37, 111, 0.0),\n   ('/api/facebook', '/api/flickr', 0, 24, 71, 0.0)])]"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mashup_count_pairs = {}\n",
    "for pl in data:\n",
    "    temp_pair = (pl[0], pl[1], pl[2])\n",
    "    for mashup in api_pair_mashups[temp_pair]:\n",
    "        if mashup in mashup_count_pairs:\n",
    "            mashup_count_pairs[mashup].append(pl)\n",
    "        else:\n",
    "            mashup_count_pairs[mashup] = [pl]\n",
    "\n",
    "list(mashup_count_pairs.items())[:3]\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "False"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_accessible('Mashup: Best Buys')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 10.30 用于表格的数据\n",
    "为了制作表格，需要完善一些数据。\n",
    "\n",
    "但是这些数据涉及到死亡时间，读取mashup的csv文件更直接"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "with open('graph_data_backup/mashup_nodes_estimator2.csv', 'r', encoding='utf-8') as csvf:\n",
    "    mashup_nodes = pd.read_csv(csvf, encoding='utf-8', parse_dates=['st', 'et', 'oet'])\n",
    "\n",
    "# 精简掉\n",
    "mashup_nodes = mashup_nodes[['name', 'st', 'et', 'oet']]\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7766\n"
     ]
    }
   ],
   "source": [
    "submit_dead_time_dict = {}\n",
    "\n",
    "for mashup_name, group in mashup_nodes.groupby('name'):\n",
    "    dates = (group['st'], group['et'], group['oet'])\n",
    "    submit_dead_time_dict[mashup_name] = dates\n",
    "\n",
    "print(len(submit_dead_time_dict))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "现在使用submit_dead_time_dict即可获取到三个时间的列表，0~2号元素分别为：[发布时间, 估测死亡时间, pw提供的死亡时间]\n",
    "\n",
    "但是et和oet都可能是2222年，所以需要多一步判断？"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "909   2012-12-20\n",
      "Name: st, dtype: datetime64[ns] 909   2222-02-22\n",
      "Name: oet, dtype: datetime64[ns]\n",
      "76399.0\n"
     ]
    }
   ],
   "source": [
    "# 试一下如何使用这个时间，然后如何转换为“天”为单位\n",
    "dates_list = list(submit_dead_time_dict.values())\n",
    "dates1 = dates_list[0]\n",
    "st1, oet1 = dates1[0], dates1[2]\n",
    "print(st1, oet1)\n",
    "delta1 = oet1 - st1\n",
    "print(delta1.values[0] / np.timedelta64(1, 'D'))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "# 统计一下mashup平均存活时间\n",
    "# 这里的存活时间是pw的\n",
    "# 为了方便统计多种情况写一个函数\n",
    "def get_table_data(api_pairs_list, dead_time='oet'):\n",
    "    new_pair_list = []\n",
    "    npday = np.timedelta64(1, 'D')\n",
    "    for pair in api_pairs_list:\n",
    "        temp_pair = (pair[0], pair[1], pair[2])\n",
    "        using_mashup_names = api_pair_mashups[temp_pair]    # 获得了names，要获得详情\n",
    "        if len(using_mashup_names) == 0:\n",
    "            print(temp_pair)\n",
    "            continue\n",
    "        act_use = 0\n",
    "        all_use = 0\n",
    "        alive_days_list = []\n",
    "        for name in using_mashup_names:\n",
    "            # mashup_obj = mashups_name_dict[name]\n",
    "            st, et, oet = submit_dead_time_dict.get(name, (None, None, None))\n",
    "            # st, et, oet = st.dt.year.values[0], et.dt.year.values[0], oet.dt.year.values[0]\n",
    "            if st is None:\n",
    "                # 这个mashup不在csv文件中，直接舍弃\n",
    "                continue\n",
    "            # st 不是None，all_use += 1\n",
    "            all_use += 1\n",
    "            if dead_time == 'oet':\n",
    "                # 用oet判断存活与否\n",
    "                if oet.dt.year.values[0] > 2200:\n",
    "                    # 活着的mashup\n",
    "                    act_use += 1\n",
    "                    # alive_days = '-' # 到目前依然存活\n",
    "                else:\n",
    "                    # 已死亡的mashup\n",
    "                    alive_days = oet - st\n",
    "                    alive_days_list.append(alive_days.values[0] / npday)\n",
    "            elif dead_time == 'et':\n",
    "                # 用et判断存活与否\n",
    "                if et.dt.year.values[0] > 2200:\n",
    "                    # 活着的mashup\n",
    "                    act_use += 1\n",
    "                else:\n",
    "                    # 已死亡的mashup\n",
    "                    alive_days = et - st\n",
    "                    alive_days_list.append(alive_days.values[0] / npday)\n",
    "            else:\n",
    "                print('Use et or oet')\n",
    "                return None\n",
    "        if all_use == 0:\n",
    "            print(temp_pair)\n",
    "            continue\n",
    "        dead_use = all_use - act_use\n",
    "        if dead_use == 0:\n",
    "            new_pair = (pair[0], pair[1], pair[2], act_use, all_use, act_use / float(all_use), -1)\n",
    "        else:\n",
    "            new_pair = (pair[0], pair[1], pair[2], act_use, all_use, act_use / float(all_use), sum(alive_days_list) / dead_use)\n",
    "        new_pair_list.append(new_pair)\n",
    "\n",
    "    return new_pair_list"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('/api/typekey', '/api/paypal', 0)\n",
      "('/api/paypal', '/api/typekey', 0)\n",
      "('/api/shoppingcom', '/api/amazon-a9-opensearch', 0)\n",
      "('/api/amazon-a9-opensearch', '/api/shoppingcom', 0)\n",
      "('/api/alexa-web-search', '/api/amazon-product-advertising', 0)\n",
      "('/api/amazon-product-advertising', '/api/alexa-web-search', 0)\n",
      "('/api/openlayers', '/api/yahoo-maps', 0)\n",
      "('/api/yahoo-maps', '/api/openlayers', 0)\n",
      "('/api/openlayers', '/api/microsoft-bing-maps', 0)\n",
      "('/api/openlayers', '/api/nasa', 0)\n",
      "('/api/nasa', '/api/openlayers', 0)\n",
      "('/api/microsoft-bing-maps', '/api/openlayers', 0)\n",
      "('/api/lyricsfly', '/api/seeqpod', 0)\n",
      "('/api/lyricsfly', '/api/amazon-simpledb', 0)\n",
      "('/api/seeqpod', '/api/lyricsfly', 0)\n",
      "('/api/amazon-simpledb', '/api/lyricsfly', 0)\n",
      "('/api/smugmug', '/api/google-maps', 0)\n",
      "('/api/google-maps', '/api/smugmug', 0)\n",
      "('/api/eventful', '/api/microsoft-mappoint', 0)\n",
      "('/api/microsoft-mappoint', '/api/eventful', 0)\n",
      "('/api/delicious-0', '/api/amazon-a9-opensearch', 0)\n",
      "('/api/amazon-a9-opensearch', '/api/google-maps', 0)\n",
      "('/api/yahoo-traffic', '/api/amazon-a9-opensearch', 0)\n",
      "('/api/amazon-a9-opensearch', '/api/delicious-0', 0)\n",
      "('/api/google-maps', '/api/amazon-a9-opensearch', 0)\n",
      "('/api/amazon-a9-opensearch', '/api/yahoo-traffic', 0)\n"
     ]
    }
   ],
   "source": [
    "table_data = get_table_data(api_pairs_list, dead_time='et')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "[('/api/google-maps',\n  '/api/twitter',\n  0,\n  63,\n  185,\n  0.34054054054054056,\n  1405.0901639344263),\n ('/api/twitter',\n  '/api/google-maps',\n  0,\n  63,\n  185,\n  0.34054054054054056,\n  1405.0901639344263),\n ('/api/flickr',\n  '/api/youtube',\n  0,\n  36,\n  184,\n  0.1956521739130435,\n  1589.1554054054054),\n ('/api/youtube',\n  '/api/flickr',\n  0,\n  36,\n  184,\n  0.1956521739130435,\n  1589.1554054054054),\n ('/api/facebook',\n  '/api/twitter',\n  0,\n  64,\n  179,\n  0.3575418994413408,\n  1414.2608695652175),\n ('/api/twitter',\n  '/api/facebook',\n  0,\n  64,\n  179,\n  0.3575418994413408,\n  1414.2608695652175)]"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(table_data, key=lambda x: x[4], reverse=True)[:6]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# 保存一下\n",
    "import pickle"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "# 防止不小心运行就注释掉了，如需导出再取消注释\n",
    "# table_file = open('table_data et.pkl', 'wb')\n",
    "# pickle.dump(table_data, table_file)\n",
    "# table_file.close()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "[('/api/google-maps',\n  '/api/twitter',\n  0,\n  63,\n  185,\n  0.34054054054054056,\n  1405.0901639344263),\n ('/api/twitter',\n  '/api/google-maps',\n  0,\n  63,\n  185,\n  0.34054054054054056,\n  1405.0901639344263),\n ('/api/flickr',\n  '/api/youtube',\n  0,\n  36,\n  184,\n  0.1956521739130435,\n  1589.1554054054054),\n ('/api/youtube',\n  '/api/flickr',\n  0,\n  36,\n  184,\n  0.1956521739130435,\n  1589.1554054054054),\n ('/api/facebook',\n  '/api/twitter',\n  0,\n  64,\n  179,\n  0.3575418994413408,\n  1414.2608695652175),\n ('/api/twitter',\n  '/api/facebook',\n  0,\n  64,\n  179,\n  0.3575418994413408,\n  1414.2608695652175),\n ('/api/flickr',\n  '/api/google-maps',\n  0,\n  47,\n  169,\n  0.2781065088757396,\n  1608.622950819672),\n ('/api/google-maps',\n  '/api/flickr',\n  0,\n  47,\n  169,\n  0.2781065088757396,\n  1608.622950819672),\n ('/api/youtube',\n  '/api/google-maps',\n  0,\n  48,\n  157,\n  0.3057324840764331,\n  1596.5045871559632),\n ('/api/google-maps',\n  '/api/youtube',\n  0,\n  48,\n  157,\n  0.3057324840764331,\n  1596.5045871559632)]"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('table_data et.pkl', 'rb') as tbf:\n",
    "    tda = pickle.load(tbf)\n",
    "sorted(tda, key=lambda x: x[4], reverse=True)[:10]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "[('/api/google-maps', '/api/twitter', 0, 63, 185, 0.34, 1405.09),\n ('/api/twitter', '/api/google-maps', 0, 63, 185, 0.34, 1405.09),\n ('/api/flickr', '/api/youtube', 0, 36, 184, 0.2, 1589.16),\n ('/api/youtube', '/api/flickr', 0, 36, 184, 0.2, 1589.16),\n ('/api/facebook', '/api/twitter', 0, 64, 179, 0.36, 1414.26),\n ('/api/twitter', '/api/facebook', 0, 64, 179, 0.36, 1414.26),\n ('/api/flickr', '/api/google-maps', 0, 47, 169, 0.28, 1608.62),\n ('/api/google-maps', '/api/flickr', 0, 47, 169, 0.28, 1608.62),\n ('/api/youtube', '/api/google-maps', 0, 48, 157, 0.31, 1596.5),\n ('/api/google-maps', '/api/youtube', 0, 48, 157, 0.31, 1596.5)]"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tda = [(line[0], line[1], line[2], line[3], line[4], round(line[5], 2), round(line[6], 2)) for line in tda]\n",
    "sorted(tda, key=lambda x: x[4], reverse=True)[:10]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# 去除对称的\n",
    "\n",
    "data = tda\n",
    "# data = api_pairs_list\n",
    "for d in data:\n",
    "    rev_d = (d[1], d[0], d[2], d[3], d[4], d[5], d[6])\n",
    "    if rev_d in data:\n",
    "        data.remove(d)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "# 查看这个数据里面有没有存活率特别高的\n",
    "high_ratio_low_use = sorted([p for p in tda if 10 <= p[4] <= 80 and p[5] >= 0.5], key=lambda x: x[5], reverse=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "([('/api/twitter', '/api/google-maps', 0, 63, 185, 0.34, 1405.09),\n  ('/api/youtube', '/api/flickr', 0, 36, 184, 0.2, 1589.16),\n  ('/api/twitter', '/api/facebook', 0, 64, 179, 0.36, 1414.26),\n  ('/api/google-maps', '/api/flickr', 0, 47, 169, 0.28, 1608.62),\n  ('/api/google-maps', '/api/youtube', 0, 48, 157, 0.31, 1596.5),\n  ('/api/twilio-sms', '/api/twilio', 0, 43, 153, 0.28, 1259.22)],\n [('/api/yahoo-shopping', '/api/ebay', 0, 2, 10, 0.2, 1121.12),\n  ('/api/technorati', '/api/lastfm', 0, 4, 10, 0.4, 1788.17),\n  ('/api/delicious-0', '/api/feedburner', 0, 2, 10, 0.2, 1474.25),\n  ('/api/yahoo-search', '/api/yahoo-video-search', 0, 2, 10, 0.2, 1947.25),\n  ('/api/twitter', '/api/technorati', 0, 2, 10, 0.2, 1139.88),\n  ('/api/wikipedia', '/api/geonames', 0, 3, 10, 0.3, 1033.71)])"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:6], [d for d in data if d[4] >= 10][-6:]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "# 导出前几个为csv文件，方便转换成tex的表格\n",
    "k = -1\n",
    "with open('top all et.csv', 'w', encoding='utf-8') as tbcsv:\n",
    "    tbcsv.write('API1,API2,Active Use,Total Use,Ratio,Avg Days\\n')\n",
    "    for row in tda[:k]:\n",
    "        tbcsv.write('{},{},{},{},{},{}\\n'.format(row[0], row[1], row[3], row[4], row[5], row[6]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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